Simulation of fine organic aerosols in the western Mediterranean area during the ChArMEx 2013 summer campaign

Simulation of fine organic aerosols in the western Mediterranean area

Simulation of fine organic aerosols in the western Mediterranean area during the ChArMEx 2013 summer campaignSimulation of fine organic aerosols in the western Mediterranean areaArineh Cholakian et al.

The simulation of fine organic aerosols with
CTMs (chemistry–transport models) in the western Mediterranean basin has not
been studied until recently. The ChArMEx (the Chemistry-Aerosol Mediterranean
Experiment) SOP 1b (Special Observation Period 1b) intensive field campaign
in summer of 2013 gathered a large and comprehensive data set of observations,
allowing the study of different aspects of the Mediterranean atmosphere
including the formation of organic aerosols (OAs) in 3-D models. In this study,
we used the CHIMERE CTM to perform simulations for the duration of the SAFMED
(Secondary Aerosol Formation in the MEDiterranean) period (July to August 2013) of this campaign. In particular, we evaluated four schemes for the
simulation of OA, including the CHIMERE standard scheme, the VBS (volatility
basis set) standard scheme with two parameterizations including aging of
biogenic secondary OA, and a modified version of the VBS scheme which
includes fragmentation and formation of nonvolatile OA. The results from
these four schemes are compared to observations at two stations in the
western Mediterranean basin, located on Ersa, Cap Corse (Corsica, France),
and at Cap Es Pinar (Mallorca, Spain). These observations include OA mass
concentration, PMF (positive matrix factorization) results of different OA
fractions, and 14C observations showing the fossil or nonfossil origins
of carbonaceous particles. Because of the complex orography of the Ersa site,
an original method for calculating an orographic representativeness error
(ORE) has been developed. It is concluded that the modified VBS scheme is
close to observations in all three aspects mentioned above; the standard VBS
scheme without BSOA (biogenic secondary organic aerosol) aging also has a
satisfactory performance in simulating the mass concentration of OA, but not
for the source origin analysis comparisons. In addition, the OA sources over
the western Mediterranean basin are explored. OA shows a major biogenic
origin, especially at several hundred meters height from the surface; however
over the Gulf of Genoa near the surface, the anthropogenic origin is of
similar importance. A general assessment of other species was performed to
evaluate the robustness of the simulations for this particular domain before
evaluating OA simulation schemes. It is also shown that the Cap Corse site
presents important orographic complexity, which makes comparison between model
simulations and observations difficult. A method was designed to estimate an
orographic representativeness error for species measured at Ersa and yields
an uncertainty of between 50 and 85 % for primary pollutants, and around
2–10 % for secondary species.

The Mediterranean basin is subject to multiple emission sources;
anthropogenic emissions that are transported from adjacent continents or are
produced within the basin, local or continental biogenic and natural
emissions among which the dust emissions from northern Africa can be
considered as an important source (Pey et al., 2013;
Vincent et al., 2016). All
these different sources, the geographic particularities of the region
favoring accumulation of pollutants
(Gangoiti et al., 2001), and the
prevailing meteorological conditions favorable to intense photochemistry and
thus secondary aerosol formation, make the Mediterranean area a region
experiencing a heavy burden of aerosols
(Monks et al., 2009; Nabat et al., 2012). In the
densely populated coastal areas, this aerosol burden constitutes a serious
sanitary problem considering the harmful effects of fine aerosols on human
health (Martinelli et al., 2013). In addition, studies have shown that the
Mediterranean area could be highly sensitive to future climate change effects
(Giorgi, 2006; Lionello and Giorgi, 2007). This could affect aerosol formation processes, but in turn the
aerosol load also affects regional climate
(Nabat et al., 2013). These interactions, the high aerosol burden in the area, and its health impact
related to the high population density situated around the basin make this
region particularly important to study.

The aforementioned primary emissions can be in the form of gaseous species, as
particulate matter, or as semi-volatile species distributed between both
phases (Robinson et al., 2007). In the
atmosphere, they can subsequently undergo complex chemical processes
lowering their volatility, which leads to the formation of secondary particles.
These processes are not entirely elucidated especially for the formation of
SOAs (secondary organic aerosols) for example
(Kroll and Seinfeld, 2008), starting from initially emitted biogenic or anthropogenic VOCs (volatile
organic compounds) and SVOCs (semi-volatile organic compounds).

The chemical composition of aerosols has been studied in detail in the
eastern Mediterranean area (Lelieveld et al., 2002; Bardouki et al., 2003;
Sciare et al., 2005, 2008; Koçak et al., 2007; Koulouri et al., 2008) and to a lesser extent in the western part (Sellegri et al., 2001;
Querol et al., 2009; Minguillón et al., 2011; Ripoll et al., 2014; Menut
et al., 2015; Arndt et al., 2017). Little focus has been given to the
formation of organic aerosol (OA) over the western Mediterranean even if OA
can play a significant role in both local and global climate (Kanakidou et
al., 2005) and can affect health (Pöschl, 2005; Mauderly and Chow, 2008).
Its contribution has been calculated in studies to be nearly 30 % in
PM1 for the eastern Mediterranean area during the FAME 2008
campaign (i.e., Hildebrandt et al., 2010). It is also important to know the
contribution of different sources (biogenic, anthropogenic) to the total
concentration of OA in the western part of the basin. Such studies have been
performed for the eastern Mediterranean basin (e.g., Hildebrandt et al.,
2010), while for the western part of the basin, they have been in general
restricted to coastal cities such as Marseilles and Barcelona (El Haddad et
al., 2011; Mohr et al., 2012; Ripoll et al., 2014).

The ChArMEx (the Chemistry Aerosol Mediterranean Experiment;
http://charmex.lsce.ipsl.fr, last access: 17 August 2016) project was organized in this context,
with a focus over the western Mediterranean basin during the period of
2012–2014, in order to better assess the sources, formation, transformation,
and mechanisms of transportation of gases and aerosols. During this project,
detailed measurements were acquired not only for the chemical composition of
aerosols but also for a large number of gaseous species from both
ground-based and airborne platforms. The project ChArMEx is divided into
different sub-projects, each with a different goal; among those, the SAFMED
(Secondary Aerosol Formation in the MEDiterranean) project aimed at
understanding and characterizing the concentrations and properties of OA in
the western Mediterranean (for example Nicolas, 2013;
Di Biagio et al., 2015;
Chrit et al., 2017; Arndt et al., 2017; Freney et al., 2017). To reach these goals, two intense ground-based and airborne campaigns
were organized during July–August of 2013 and also summer of 2014. The focus
of the present study is on the SAFMED campaign in summer of 2013, since
detailed measurements on the formation of OA and precursors were obtained
during this period, namely at Ersa (Corsica) and Es Pinar (Mallorca). Other
ChArMEx sub-projects and campaigns included the TRAQA (Transport et
Qualité de l'Air) campaign in summer 2012, set up to study the transport
and impact of continental air on atmospheric pollution over the basin
(Sič et al., 2016), and the ADRIMED (Aerosol Direct Radiative Impact on the
regional climate in the
MEDiterranean, June–July 2013) campaign aimed at understanding and
assessing the radiative impact of various aerosol sources
(Mallet et al., 2016).

Modeling of aerosol processes and properties is a difficult task. Aside from
the lack of knowledge of aerosol formation processes, the difficulty lies in
the fact that OAs present an amalgam of thousands of different
species that cannot all be represented in a 3-D chemistry–transport model (CTM) due
to limits in computational resources. Therefore a small number of lumped
species with characteristics that are thought to be representative of all the
species in each group are used instead. There are many different approaches
that can be used in creating representative groups for OAs
(e.g.,
which characteristics to use to group the species, which species to lump
together, physical processes that should be presented for their
simulation). It is therefore necessary to test these different
simulation schemes for OAs in different regions and compare the
results to experimental data to check for their robustness. For example,
Chrit et al. (2017) modeled SOA formation in the western Mediterranean area
during the ChArMEx summer campaigns, with a surrogate scheme that also
contains ELVOCs (extremely low-volatility organic compounds). The simulated
concentrations and properties (oxidation and affinity with water) of OAs agree well with the observations performed at Ersa (Corsica), after
they had included the formation of extremely low-volatility OAs
and organic nitrate from monoterpene oxidation in the model.

The present study focuses on the comparison of different OA formation schemes
implemented in the CHIMERE chemistry–transport model for simulation of OA
over the western Mediterranean area. Different configurations of the
volatility basis set (VBS; Donahue et al., 2006; Robinson et al., 2007;
Shrivastava et al., 2013) and the base parameterization of the CHIMERE 3-D
model (Bessagnet et al., 2008; Menut et al., 2013) are used for this purpose.
Our work takes advantage of the extensive experimental data pool obtained
during the SAFMED campaign. This enables us to perform model–observation
comparisons with unprecedented detail over this region, including not only
the OA concentration but also its origin (14C analyses) and its
oxidation state (positive matrix factorization (PMF) method results). In addition, a comparison for
meteorological parameters and gaseous or particulate species which could affect
the production of OA or could help analyze the robustness of the used schemes
has been performed. Moreover, because of the orographic complexity of one of
the sites (Ersa, Cap Corse) explored in this work, a novel method is designed
to calculate the orographic representativeness error of different species. To
the best of our knowledge, this is the first time that the concentrations of
precursors, intermediary products, and OA concentrations and properties have
been simulated for different OA simulation schemes and compared for each
scheme to multiple series of measurements at different stations for the
western Mediterranean area. For OA schemes, the paper aims at assessing the
robustness of each scheme with regard to different criteria as mass, fossil,
and modern fraction and volatility. Section 2 describes the model and the
inputs used for the simulations. Also, the evaluated schemes are explained in
this section in more detail. The experimental data used for
simulation–observation comparisons are discussed in Sect. 3. An overall
validation of the model is presented in Sect. 4, together with comparisons of
different gaseous and particulate species and meteorological parameters to
observations. In Sect. 5, comparison of implemented schemes to measurements
regarding concentration, oxidation state, and origin of OA is
presented. In Sect. 6, the contribution of different sources to the OA present in the whole basin is explored, before the conclusion in
Sect. 7.

The CHIMERE model (Menut et al., 2013;
http://www.lmd.polytechnique.fr/chimere, last access: 9 August 2016) is an offline regional CTM
which has been tested rigorously for Europe and
France (Zhang et al., 2013; Petetin et al., 2014; Colette et al., 2015;
Menut et al., 2015; Rea et al., 2015). It is also widely used in both research and forecasting
activities in France, Europe, and other countries (Hodzic and Jimenez, 2011).
In this work, a slightly modified version of the CHIMERE 2014b configuration
is used to perform the simulations. The modifications concern an updated
description of the changes in aerosol size distribution due to condensation
and evaporation processes (Mailler et al., 2017). Four domains are used in
the simulations: a coarse domain covering all of Europe and northern Africa
with a 30 km resolution, and three nested domains inside the coarse domain
with resolutions of 10, 3, and 1 km (Fig. 1). The 10 km resolved domain
covers the western Mediterranean area and the two smaller domains are
centered on the Cap Corse, where the main field observations in SAFMED were
performed. Such highly resolved domains are necessary to resolve the complex
orography of the Cap Corse ground-based measurement site, which will be discussed
in Sect. 4, while for the flatter Es Pinar site, a 10 km resolution is
sufficient. The simulations for each domain contain 15 vertical levels
starting from 50 m to about 12 km above sea level (a.s.l.) with an average
vertical resolution of 400 m within the continental boundary layer and
1 km above. The CHIMERE model needs a set of gridded
data as mandatory input: meteorological data, emission data for both biogenic and anthropogenic
sources, land use parameters, boundary and limit conditions, and other optional
inputs such as dust and fire emissions. Given these inputs, the model
produces the concentrations and deposition fluxes for major gaseous and
particulate species and also intermediate compounds. The simulations
presented in this article cover the period of 1 month from 10 July to
9 August.

Figure 1The four domains used in simulations (D30, D10, D3, D1). The resolution for each domain is given in the table below the image.

Anthropogenic emissions for all but shipping SNAP (Selected Nomenclature for
Air Pollution) sectors come from the HTAP-V2 (Hemispheric Transport of Air
Pollution;
http://edgar.jrc.ec.europa.eu/htap_v2/index.php, last access: 21 August 2016) inventory. The shipping sector in this inventory
was judged to overestimate ship traffic around the Cap Corse area, especially
on the shipping lines between Marseilles and Corsica, due to
overweighing ferries with respect to cargos (Van der Gon, personal
communication). This could be explained by the fact that the boat traffic
description is based on voluntary information. Therefore HTAP-V2 shipping
emissions were replaced by those of the MACC-III inventory (Kuenen et al.,
2014). The base resolution of the HTAP inventory is 10 km × 10 km and
that of the MACC-III inventory is 7 km × 7 km. For both inventories
the emissions for the year 2010 were used since this year was the latest
common year in the two inventories.

Biogenic emissions are calculated using MEGAN (Model of Emissions of Gases
and Aerosols from Nature; Guenther et al., 2006) including isoprene,
limonene, α-pinene, β-pinene, ocimene, and other monoterpenes
with a base horizontal resolution of
0.008∘× 0.008∘. The land use data come from
GlobCover (Arino et al., 2008) with a base resolution of
300 m × 300 m. Initial and boundary conditions of chemical species
are taken from the climatological simulations of LMDz-INCA3
(Hauglustaine et al., 2014) for gaseous species and GOCART
(Chin et al., 2002) for particulate matter.

The chemical mechanism used for the baseline gas-phase chemistry is the
MELCHIOR2 scheme (Derognat et al., 2003). This mechanism has around 120
reactions to describe the whole gas-phase chemistry. The reaction rates used
in MELCHIOR are constantly updated (last update in 2015); however, the
reaction scheme itself has not been updated since 2003. Some reactions have
been added to it by Bessagnet et al. (2009) regarding the oxidation of
OA precursors, but they do not affect gas-phase chemistry. Also,
MELCHIOR has been compared to SAPRC-07A, a more recent scheme (Carter, 2010),
and the results show acceptable differences between the two schemes; for
example, when compared to EEA (European Economic Area) ozone measurements,
both produce a correlation coefficient of 0.71. These comparisons are
presented in Menut et al. (2013) and Mailler et al. (2017).

The CHIMERE aerosol module is responsible for the simulation of physical and
chemical processes that influence the size distribution and chemical
speciation of aerosols (Bessagnet et al., 2008). This module distributes
aerosols in a number of size bins; here 10 bins range from 40 nm to
40 µm, in a logarithmic sectional distribution, each bin spanning
over a size range of a factor of 2 (40–20, 20–10 µm, etc.).
The module also addresses coagulation, nucleation, condensation, and
dry and wet deposition processes. The basic chemical speciation includes
elemental carbon (EC), sulfate, nitrate, ammonium, SOA, dust, salt, and PPM
(primary particulate matter other than ones mentioned above).

2.1 Organic aerosol simulation

The SOA particles are divided, depending on their precursors, into two
groups: ASOA (anthropogenic SOA) and BSOA (biogenic SOA). Four schemes were
tested to simulate their formation; more detail on each scheme is presented
below.

2.1.1 CHIMERE standard scheme

The SOA simulation scheme in CHIMERE (Bessagnet et
al., 2008) consists of a single-step oxidation process in which VOC lumped
species are directly transformed into SVOCs
with yields that are taken from experimental data
(Odum et al., 1997; Griffin et al., 1999; Pun and Seigneur, 2007).
These SVOC species are then distributed into gaseous and particulate phases
(Fig. 2a) following the partitioning theory of Pankow
(Pankow, 1987). The precursors for this scheme are presented in the Supplement
(Sect. SI1). A number of 11 semi-volatile surrogate compounds are formed from
these precursors, which include six hydrophilic species, three hydrophobic
species, and two surrogates for isoprene oxidation. The sum of all 11 species
results in the concentration of simulated SOA in this scheme.

Figure 2Organic aerosol simulation schemes. (a) CHIMERE standard scheme
(Bessagnet et al., 2008): from a parent volatile organic compound (VOC),
different semi-volatile organic compounds (SVOCs) (only one represented) are
formed in a single step by oxidation; they are in equilibrium between gas and
aerosol phases (pSVOC); (b) VBS standard scheme (Robinson et al., 2007): from
a parent VOC, SVOCs with regularly spaced volatility ranges are formed and are
in equilibrium with the aerosol phase. Aging of SVOC by functionalization is
included by passing species to classes with lower volatility; (c) modified VBS
scheme (Shrivastava et al., 2015): here SVOC aging also includes
fragmentation, leading to transfer of species to classes with higher
volatility. In addition, semi-volatile aerosol can be irreversibly
transformed into nonvolatile aerosol (yellow-filled circle). For each bin,
saturation concentration is shown in micrograms per cubic meter. Note that this schematic
represents BSOA and ASOA (biogenic and anthropogenic secondary organic aerosol) where
four bins are used; for SVOC and IVOC (semi-volatile and intermediate-volatility organic
compounds, where nine bins are used) a schematic is presented in Supplement Sect. SI1.

2.1.2 VBS scheme

As an alternative to single-step schemes like the one in CHIMERE, the VBS approach was developed. In these types of schemes,
SVOCs are divided into volatility bins regardless of their chemical
characteristics, but only depending on their saturation concentration.
Therefore it becomes possible to add aging processes in the simulation of OA
by adding reactions that shift species from one volatility bin to another
(Donahue et al., 2006). This scheme was implemented and tested in CHIMERE for
Mexico City (Hodzic and Jimenez, 2011) and the Paris region (Zhang et al.,
2013). The volatility profile used for this scheme consists of nine
volatility bins with saturation concentrations in the range of 0.01 to
106µg m−3 (convertible to saturation pressure using
atmospheric standard conditions), across which the emissions of SVOCs and
IVOCs are distributed, following a
specific aggregation table (Robinson et al., 2007). Four volatility bins are
used for ASOA and BSOA ranging from 1 to
1000 µg m−3. SOA yields are taken from the literature (Lane
et al., 2008; Murphy and Pandis, 2009) using low-NOx conditions
(VOC ∕ NOx>10 ppbC ppb−1). The SVOC species can age, by
decreasing their volatility by one bin independent of their origin with a
given constant rate. SVOC species are either directly emitted or formed from
anthropogenic or biogenic VOC precursors. Fragmentation processes and the
production of nonvolatile SOAs are ignored in this scheme. In the basic VBS
scheme, the BSOA aging processes are usually ignored since they tend to
result in a significant overestimation of BSOA
(Lane et al., 2008). Although physically present, their kinetic constants for this aging process are
considered the same as anthropogenic compounds and seem to be overestimated.
However, in Zhang et al. (2013), including BSOA aging was necessary to
explain the observed experimental data. Therefore, in this work, the VBS
scheme is evaluated both with and without including the BSOA aging processes.
Figure 2b shows a simplified illustration for ASOA and BSOA,
while the partition for SVOC is presented in the Supplement (Sect. SI2). For all
bins, regardless of their origin, the partitioning between gaseous and the
particulate phases is performed following Raoult's law and depends on total
OA concentration. Under normal atmospheric conditions, SVOC with
the volatility range of 0.01 to 103µg m−3 can form
aerosols. In total, the sum of 24 species in the model (with 10 size
distribution bins each, i.e., 240 species in total) makes up the total
concentration of SOA simulated by this scheme.

2.1.3 Modified VBS scheme

The basic VBS scheme does not include fragmentation processes, corresponding
to the breakup of oxidized OA compounds in the atmosphere into smaller and
thus more volatile molecules (Shrivastava et al., 2011). It also does not
include the formation of nonvolatile SOA, where SOA can become nonvolatile
after formation (Shrivastava et al., 2015). In this work, these two processes
were added to the VBS scheme presented above and tested for the Mediterranean
basin. The volatility bins for the VBS model were not changed (ranges
presented in the previous section). SOA yields were kept as in the standard
VBS scheme; however, instead of using the low-NOx or the
high-NOx regimes, an interpolation between the yields of these
two regimes was added to the model. For this purpose, a parameter is added to
the scheme, which calculates the ratio of the reaction rate of RO2
radicals with NO (high-NOx regime) with respect to the sum of
reaction rates of the reactions with HO2 and RO2
(low-NOx regime). For this purpose, a parameter (α) is
added to the scheme, which calculates the ratio of the reaction rate of
RO2 radicals with NO (νNO; high-NOx regime)
with respect to the sum of reaction rates of the reactions with HO2
(νHO2) and RO2 (νRO2;
low-NOx regime). The parameter α is expressed as follows:

(1)α=νNOνNO+νHO2+νRO2.

This α value represents the part of RO2 radicals reacting
with NO (which leads to applying “high NOx yields”). It is
calculated for each grid cell by using the instantaneous NO, HO2,
and RO2 concentrations in the model. Then, the following equation
is used to calculate an adjusted SOA yield using this α value
(Carlton et al., 2009).

(2)Y=α×YhighNOx+(1-α)×YlowNOx

The fragmentation processes for the SVOC start after the third generation of
oxidation because fragmentation is favored with respect to functionalization
for more oxidized compounds. Therefore, three series of species in different
volatility bins were added to present each generation, similar to the
approach setup in Shrivastava et al. (2013). For biogenic VOC, fragmentation
processes come into effect starting from the first generation, as in
Shrivastava et al. (2013), because the intermediate species are considered to
be
more oxidized. A fragmentation rate of 75 % (with 25 % left for
functionalization) is used in this work for each oxidation step following
Shrivastava et al. (2015). The formation of nonvolatile SOA is performed by
moving a part of each aerosol bin to nonvolatile bins with a reaction
constant corresponding to a lifetime of 1 h, similar to Shrivastava et
al. (2015). Figure 2c shows a scheme of the modified VBS for the VOC. In
total, 40 species (with 10 size distribution bins each, i.e., 400 species in
total) are added together to calculate the total concentration of SOA
simulated by this scheme. The resulting model has a total of 740 species in
the output files (including gas-phase chemistry), which makes this scheme the
most time consuming among the tested schemes. In Sect. 5 of this paper, the
results from the three schemes introduced above will be compared to
observations.

During the SAFMED sub-project, measurements were made at two major sites, the
Ersa, Cap Corse, station and the Cap Es Pinar, Mallorca, station. The
geographical characteristics and the measurements performed at each site are
presented in the following section.

3.1 ChArMEx measurements

The Ersa supersite (42∘58′04.1′′, 9∘22′49.1′′) is
located on the northern edge of Corsica, in a rural environment,
at an altitude of 530 m a.s.l. The station is located on a crest that
dominates the northern part of the cape. It has a direct view of the sea on
the western, northern, and eastern sides. Measurements carried out in
this station are reported in Table 1. More details about the instrumental
setup in Ersa can be found in
Michoud et
al. (2017) and Arndt et al. (2017).

Table 1Gas and aerosol and meteorological measurements used for this study.

The Es Pinar supersite (39∘53′04.6′′, 3∘11′40.9′′) is
located on the northeastern part of Mallorca. The monitoring station was
placed in the “Es Pinar” military facilities belonging to the Spanish
Ministry of Defense. The environment is a non-urbanized area surrounded by
pine forested slopes and is one of the most insulated zones on Mallorca, in between the Alcúdia and Pollença bays, but can still be
influenced by local anthropogenic emissions. The site is located at an
altitude of 20 m a.s.l. The location of the station and a list of available
measurements are presented in Fig. 3 and Table 1, respectively.

At Ersa, NOx (nitrogen oxides) were measured using a CraNOx analyzer
using ozone chemiluminescence with a resolution of 5 min. The photolytic
converter in the analyzer allows the conversion of direct measurements of
NO2 into NO in a selective way, thus avoiding interferences with
other NOy species. At Es Pinar, an API Teledyne T200 with
molybdenum converter was used; therefore, the measurements are not specific
to NO2 and interferences of NOy are possible for these
measurements. VOCs were monitored at both supersites using
a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) (Kore™ second generation at Ersa,
and Ionikon™ PTR-ToF-MS 8000 at Es Pinar). A
detailed procedure of VOC quantification is provided in Michoud et
al. (2017) for Ersa. Briefly, both
instruments were calibrated daily using gas standard calibration bottles and
blanks performed by means of a catalytic converter (stainless steel tubing
filled with Pt wool held at 350 ∘C).

A quadruple aerosol chemical speciation monitor (Q-ACSM, Aerodyne Research
Inc.; Ng et al., 2011) was used for the measurements of the chemical
composition of non-refractory submicron aerosol at Ersa with a time
resolution of 30 min. This instrument has the same general structure of an
AMS (aerosol mass spectrometer), with the difference that it was developed
specifically for long-term monitoring. A high-resolution time-of-flight AMS
(HR-ToF-AMS, Aerodyne Research Inc.; Decarlo et al., 2006) operated under
standard conditions (i.e., temperature of the vaporizer set at
600 ∘C, electronic ionization (EI) at 70 eV) was deployed with a
temporal resolution of 8 min to determine the bulk chemical composition of
the non-refractory fraction of the aerosol for the Es Pinar site. AMS data
were processed and analyzed using the HR-ToF-AMS analysis software SQUIRREL
(SeQUential Igor data RetRiEvaL) v.1.52L and PIKA (Peak Integration by Key
Analysis) v.1.11L for the IGOR Pro software package (Wavemetrics, Inc., Portland,
OR, USA). Q-ACSM and AMS source apportionment results discussed in this work
are detailed in Michoud et al. (2017). For
both sites, source contributions were obtained from PMF analysis (Paatero and
Tapper, 1994) of Q-ACSM and AMS OA mass spectra. PMF was solved using the
multi-linear engine algorithm (ME-2; Paatero, 1997), using the Source Finder
toolkit (SoFi; Canonaco et al., 2013). For the Ersa site, the HOA
(hydrocarbon-like organic aerosol) profile was constrained with a reference
HOA factor using an a value of 0.1. The a value refers to the extent to which
the output HOA factor is allowed to vary from the input HOA reference mass
spectra (i.e., 10 % in this case; Canonaco et al., 2013). In such a remote
environment, the HOA factor could not be extracted from the OA mass spectral
matrix with a classic unconstrained PMF approach. Two other factors were
extracted, without any constrains, including SVOOA (semi-volatile oxygenated
organic aerosol) and LVOOA (low-volatility oxygenated organic aerosol). For the
Es Pinar site, HOA has been constrained using an a value of 0.05. Three
additional factors were retrieved, including an SVOOA and two LVOOA factors. In addition to HOA, three other factors have been extracted
from the PMF analysis: one SVOOA and
two
LVOOA factors. Differences between
these two LVOOA factors are mainly linked to air mass origins and to the
probable influence of marine emissions. For the marine LVOOA factor at both
sites, a correlation coefficient (R2) of 0.43 and 0.47 with the main
fragment derived from methane sulfonic acid (MSA, fragment
CH3SO2+) was found for Es Pinar and Ersa, respectively. For the sake of
clarity and for the purpose of intercomparison with the model outcomes, we
merge the two LVOOA factors into one LVOOA for the Cap Es Pinar site to be compared
to the Ersa site results. Online aerosol chemical characterization was
complemented by an Aethalometer (AE33, MAGEE; Drinovec et al., 2015) at Es
Pinar and a multiangle absorption photometer (MAAP5012, Thermo) for the
quantification of black carbon (BC) at Ersa. PM10 total mass measurements were
taken from TEOM-FDMS (tapered element oscillating microbalance filter dynamic
measurement system) at Es Pinar and corrected by a factor obtained after
comparison to gravimetric measurements. Finally, daily PM1 aerosol
samples were collected onto 150 mm quartz fiber filters (Tissuquartz, Pall)
at both sites. A total of 18 and 8 samples were selected for Ersa and Es
Pinar, respectively, for a subsequent analysis of radiocarbon performed on
both organic carbon (OC) and EC fractions following the method developed in Zhang et
al. (2012).

3.2 Other measurements

For the validation of meteorological parameters, along with the
meteorological surface measurements performed at ChArMEx stations,
radiosonde data for three stations in the western Mediterranean basin
were used for simulation–observation comparisons for meteorological
parameters. The radiosondes are performed by Météo France at the
two stations of Ajaccio, France (41∘55′5′′, 8∘47′38′′),
and Nîmes, France (43∘51′22′′, 4∘24′22′′), and by
AEMet at Palma, Spain (39∘36′21′′, 2∘42′24′′). Each day
two balloons at about 00:00 and 12:00 UTC are available for each station; a
total of 96 balloons are included in the comparisons. Ajaccio and Palma are
coastal stations, but Nîmes is farther from the coast compared to the
other two stations. Each day two balloons were launched at about 00:00 and
12:00 UTC at each station, and a total of 96 balloons are included in the
comparisons for an altitude between the surface and 10 km.

The CHIMERE model has been previously validated for different parts of the
world (Hodzic and Jimenez, 2011; Solazzo et al., 2012; Borrego et al., 2013;
Berezin et al., 2013; Petetin et al., 2014; Rea et al., 2015; Konovalov et al., 2015;
Mallet et al., 2016). The data set presented in Sect. 3 is used for model
validation. First, a representativeness error within simulations is
calculated for a list of pollutants, which is necessary to distinguish
between uncertainties due to limitations in model resolution and due to
other reasons. Then a validation for the meteorological parameters is
presented, before comparison of simulation results to gaseous and aerosol
measurements.

4.1 Orographic representativeness of Cap Corse simulations

As explained before, during the ChArMEx campaign, an important number of
observations were made at Ersa, Cap Corse. In order to use this data set for
model evaluation, potential discrepancies due to a crude representation of
the complex orography of Cap Corse need to be minimized and quantified since
the measurements were performed on the crest line.

For the 10 km domain (D10), we noticed that there was an inconsistency
between simulated and real altitude of the cell where the Ersa site is
located, altitude being simulated at 360 m a.s.l. below the real
altitude of measurements (530 m a.s.l.). Therefore, 1 km
horizontally resolved simulations were performed for the inner domain.
However, even for the 1 km simulations the simulated altitude remains too
low (365 m a.s.l.). This error occurs because the altitude of each cell in
CHIMERE is calculated using the average of altitudes of points inside the
cell; therefore if the altitude of the ground surface inside a cell happens
to vary greatly, the average would be lower than the higher points seen in
the cell (which corresponds in our case to the Ersa site located on the
crest). In addition, the average of the marine boundary layer height is
typically around 500 m (Stull, 1988); therefore a discrepancy in the
simulated altitude could cause significant errors in the simulations. These
two reasons make it important to explore the representativeness of the
simulations regarding this station.

Figure 4Orographic representativeness error. (a) Neighboring cells used in
the orographic representativeness test; (b) an example of nonlinear
regressions performed on one time step for organic aerosol (OA, one point
corresponds to one grid cell); (c) results from all hourly simulations for OA.
In (b) and (c), the purple ribbon shows the confidence interval of the regression
results. In (c), the blue line shows the simulations at the nominal Ersa site
grid cell.

This led us to perform an orographic representativeness test on the 1 km
domain (D1) at the Ersa site. A matrix of neighboring cells around the grid
cell covering the Ersa station (up to 5 km distance) was taken (Fig. 4a),
and species concentrations were plotted against the variation of the altitude
of these different cells. The highest altitude reached by one of the cells is
about 450 m. Then, the concentration on the exact altitude (530 m) was
extrapolated using a nonlinear regression between the altitude and the
concentration of the selected cells with several different equations for each
time step. In total, nine nonlinear equations were tested, among which only
five were finally used for the calculation of the representativeness error.
For the other four equations, convergence problems occurred and no stable
solution could be found for some of the hourly time steps (see Supplement
Sect. SI3 for details). Regressions were performed separately for each of the
720 hourly time steps of the 1-month simulations. An example of this
regression for OAs for one time step and one of the equations is
shown in Fig. 4b (Eq. 1 from SI3). The results were filtered using two
criteria (convergence of regression for each time step and a correlation
coefficient between fitted and simulated points of higher than a threshold)
depending on statistical values of the regressions (see Supplement Sect. SI3 for
details) and only regressions conforming to these criteria were retained. If
at least two converging regressions were not retained for a given time step,
the results for that time step were not further used. Figure 4c shows the
compiled results for all equations and all simulation times in one time
series for total OA concentration. Note that model output was generated with
an hourly time step. Using these results, for a list of different species, an
orographic representativeness error was calculated using the average of
the difference of the upper and lower confidence intervals for all equations.
As an example, carbon monoxide, which is a well-mixed and a more stable
component in the atmosphere, presents the lowest error among the tested
species (2 %). Ozone also presents one of the lowest errors (4 %) and
nitrogen oxides one of the highest (75 %). OA, of particular
interest for this study, shows a moderate error of 10 %. In terms of
meteorological parameters, relative humidity appears more affected (relative
orographic representativeness error of 18 %) than temperature (the relative orographic representativeness error is calculated on values of
T in ∘C). A summary of results of this test is shown in Table 2.

Table 2Calculated relative orographic representativeness error (ORE) for a
list of species and meteorological parameters. MACR+MVK presents the sum of
methyl vinyl ketone and methacrolein.

A general conclusion is that secondary pollutants with higher atmospheric
lifetimes appear to be well represented from a geographic point of view. Conversely, model–observation comparisons for more reactive primary and
secondary pollutants with short lifetimes (primary such as NOx and
reactive secondary such as methyl vinyl ketone and methacrolein, MVK+MACR) should be performed with caution
keeping in mind the fact that the simulated altitude is not representative of
the orography for this specific station. This is due to the fact that
short-lived primary species have not yet had the chance to vertically mix,
if emission sources happen to be nearby (which is the case here). Vertical
layering of these concentrations then results in significant sensitivity to
the simulated altitude of a site. Conversely, secondary species, which
have partly been transported from the continental boundary layer, are
believed to be better mixed vertically. For those species, differences in the
simulated versus observed altitude lead to relatively smaller errors.

The question of which domain should be used for model–measurement
comparisons remains. As seen above, D10, despite having a sufficiently fine
resolution for most continental areas, is not capable of representing the
complex orography of Cap Corse; therefore the D1 simulation results have
been used for comparisons, except for meteorological parameters, where all
possible domains (and resolutions) are compared. The Es Pinar station does
not have the same intense altitudinal gradient seen at Ersa; therefore the
aforementioned test was not performed for this station and the D10
simulations are used for comparisons for Es Pinar.

In the following section, the confidence intervals representing the
orographic representativeness error derived in this section are
considered for the model–observation comparisons.

4.2 Meteorology evaluation

Meteorological output of the mesoscale WRF model at different resolutions has
been used as input to the CHIMERE CTM. The meteorological data used by
CHIMERE were compared to various meteorological observations such as
radiosondes and surface observations at the measurement sites. Detailed
results of these comparisons are given in the Supplement (Sect. SI4). Here, a short
overview of the results and the implications for the model ability to
simulate transport to the measurement sites is given.

Comparisons for temperature, a basic variable to control the quality of
meteorological simulations, show a good correlation for radiosonde
comparisons (typically from 0.60 to 0.85 for hourly values) and a low bias
(typically from −1.16 to −0.39 ∘C) for the three sites of Palma,
Nîmes, and Ajaccio. Wind speed shows a good correlation at higher
altitudes, and also near the surface for Nîmes and Palma for
radiosonde stations, while for the Ajaccio station the sea–land breezes
are probably not well represented in the model. For Es Pinar, the coastal
feature of the site is difficult to take into account in a 10 km
horizontally resolved simulation and leads to larger errors. Ground-based
meteorological measurements were also compared at both sites (SI4). At Ersa,
the correlation in finer domains is better than that of D10 for wind speed
(typically around 0.66 versus around 0.60). For the E-OBS network (surface
data sets provided by European Climate Assessment and Dataset, ECAD) project
for monitoring and analyzing climate extremes,
(Haylock et al., 2008; Hofstra et al., 2009) comparisons (SI4) also show a good correlation and a low bias for
temperature (correlation of 0.79 with a bias of −0.54 ∘C for mean
temperature observed for 71 stations in D10 domain), while the daily minima
seem to be underestimated (bias of −3 ∘C observed for 71
stations).

While the general comparison between the hourly meteorological fields used
as input for CHIMERE simulations and observations is in general already
satisfying, the correlation becomes higher and the bias lower when daily
averages representative for different meteorological conditions are compared
instead of hourly values.

4.3 Gaseous species

Among all the gaseous species available in the observations, four were chosen
in this study: nitrogen oxides (NOx), isoprene
(C5H8), monoterpenes, and the sum of methacrolein and methyl
vinyl ketone (hereafter called MACR+MVK). These four species were chosen
since isoprene and monoterpenes are the principal precursors for biogenic
SOA, MACR+MVK are formed during isoprene oxidation, and NOx is a
good tracer for local pollution. The comparisons for the Ersa and Es Pinar
stations are shown in Fig. 5, and statistics of the comparison are shown in
Table 3. For Ersa, the orographic representativeness errors derived in
Sect. 4.1 are also shown. In all comparisons, the results for the simulations
with the modified VBS scheme are used, but the choice of the OA
scheme only slightly affects the simulation of gaseous species (mainly via
heterogeneous reactions on aerosol surfaces included within CHIMERE).

Table 3Statistical data for time series shown in Fig. 5; Mean_obs shows
the average of observations. Values in parentheses for Es Pinar
NOx statistics show the comparison of NOy simulations
to NOx measurements.

Figure 5Time series showing the comparison of simulated (in blue) and
measured (in black) gaseous species during the ChArMEx/SAFMED campaign
period. (a) Nitrogen oxides (time series in red for Es Pinar represents
NOy concentrations); (b) isoprene; (c) MACR+MVK (methyl vinyl
ketone+methacrolein); (d) monoterpenes. Statistical data for these comparisons
are given in Table 2. The ribbon around Ersa simulations presents the orographic representativeness error.
On the right side of each time series, two points are shown presenting the average of simulations (blue circle)
and observations (black square).

Results show that there is a good correspondence between the averages of
simulated and observed nitrogen oxides at Ersa (Fig. 5a1). The low
correlation for nitrogen oxides at Ersa might be partly explained by the high
representativeness error (75 %) for this component. This is because the
altitude in the simulations is lower and therefore the emission sources are
closer in the model than they are in reality. At Es Pinar (Fig. 5a2), since
the measurements are not specific to NO2, the NOy time
series are added to the figure as well. As a consequence, if the model had no
error, the NOx observations would be expected to lie between
NOx and NOy simulations. This is the case because
NOx observations are on average 40 % higher than the
NOx simulations and 9 % lower than the NOy
simulations.

For isoprene (Fig. 5b1 and b2), a good correlation (0.76, 0.71) between
simulations and observations appears at both sites. However there is an
important overestimation (by a factor of 2.5) in the simulations for the Ersa
site, which could also be linked to the high orographic representativeness
error (85 %), and also to the fact that local emissions sources may not be
correctly taken into account in the MEGAN emission model. Conversely, at
Es Pinar isoprene is underestimated by about 25 %. The sum of MACR+MVK
(Fig. 5c1 and c2) is overestimated by about a factor of 2 at Ersa,
following the pattern of overestimation of isoprene at this site, while the
bias is small at Es Pinar. Monoterpenes (Fig. 5d1 and d2) show an
underestimation by about 70 % at both sites, observations being about 5
times larger at Ersa than at Es Pinar. Again, this could be related to the
orographic representativeness error at Ersa and to local
vegetation that was not accounted for at both sites.

Daily correlations of 0.35, 0.87, 0.85, and 0.58 (instead of 0.37, 0.76,
0.62,
and 0.35 hourly values) are seen at Ersa for nitrogen oxides, isoprene,
MACR+MVK, and monoterpenes, respectively. These values change to 0.16,
0.51, 0.72, and 0.10 for daily comparisons (instead of 0.12, 0.69, 0.41, and
0.14 when correlating hourly values) at Es Pinar. Improvements in
correlation for daily averages could be related to those in meteorological
parameters, at least for Ersa. They show the difficulty to correctly
simulate short-term (hourly) variations both in meteorological parameters
and chemical species.

A drawback of the comparisons for isoprene, and monoterpenes, contributing
respectively to 40 and 60 % of SOA simulated with the modified VBS scheme
in the western Mediterranean region, is the measurements' representativeness
restricted to local scales. A more regional evaluation of these precursor
species would have been necessary since SOA simulated and observed at Ersa
and El Pinar was partly formed far away from these sites.

As mentioned before (Sect. 2), isoprene and terpene emissions in our work are
generated by the MEGAN model (Guenther et al., 2006). Zare et al. (2012)
evaluated isoprene emissions derived from the MEGAN model coupled to the hemispheric
DEHM CTM against measurements. On average over 2006,
they found a simulated isoprene overestimation at four European sites (between a
factor of 2 and 10), good agreement (within ±30 %) at two sites, and an
underestimation at two sites (also between factors of 2 and 10). However, none of
the sites were located close to the Mediterranean Sea. Curci et al. (2010)
performed an inverse modeling study to correct European summer (May to
September) 2005 MEGAN isoprene emissions from formaldehyde vertical column
OMI measurements (given that isoprene is a major formaldehyde precursor). For
the western Mediterranean area, they found an isoprene emissions
underestimation using MEGAN of 40 % over Spain, a tendency for an
underestimation but with regional differences over Italy, and only small
differences over France. This comparison globally lends confidence to MEGAN-derived isoprene emissions. Unfortunately, to our best knowledge, no
comparable studies exist in order to validate monoterpene emissions. For the
Rome area in Italy, Fares et al. (2013) concluded for a variety of typical
Mediterranean tree and vegetation species mix, that MEGAN correctly simulated
(within 10 % error) mean observed monoterpene fluxes over the last 2
weeks of August 2007 for a variety of Mediterranean tree and vegetation
species mix, as long as a canopy model was included (as in our study).

4.4 Particulate species

Figure 6As for Fig. 5, but for particulate matter. (a) Black carbon (BC);
(b) sulfate particles. Statistical data for these comparisons are given in
Table 4.

Figure 6 shows the comparison between observations and simulations for
particulate sulfate and BC in PM1. These two species are
chosen as two important fine aerosol components, before the comparison of
OA in Sect. 5. The left panel shows the comparison for Ersa and
the right one for Es Pinar. Statistical information for these species is
given in Table 4. There is an overestimation for sulfate particles (Fig. 6b1
and b2) by about 45 %, well beyond the representativeness error for this
species (15 %). In addition, the short and sharp decreases in the measurements
of sulfates correspond to low clouds passing at the level of the station,
which are not simulated by the model. Cloud scavenging processes are already
taken into account in the model. However, because of the unique geographical
characteristics mentioned before for this site, the meteorological inputs did
not simulate these fog events and therefore cloud scavenging was not
activated in the simulation. Since these decreases concern only a small
percentage of the observations, they do not have a major effect on the
outcome of these comparisons. While this effect is very visible for sulfates,
it is less pronounced for other particulate species such as BC and
OA. A large sulfate peak simulated in the morning of 29 July is not present
in the observations; it originates in the model from an air mass arriving
from Marseille, which is both a busy harbor and an important industrial area
with large SO2 emissions. This is probably due to small errors in
the wind fields. In addition, there are two periods of overestimation of this
species in the simulations: the period of 18 to 20 July and the period of
29 July to the night of 1 August. During the second period, the ACSM
PM1 observations show concentrations of close to zero, which are
consistent with PM10 particle-into-liquid sampler ion chromatography (PILS-IC) sulfate measurements. For this period
elevated southerly winds are observed in the Corsica area, and the absence of
strong SO2 sources in this sector might explain the lower
concentrations that are seen not only for sulfate but also for BC.
The constant overestimation of sulfates during this period may suggest an
overestimation of boundary conditions for these species.

A moderate correlation (0.36) between the model and the observations is seen
for BC (Fig. 6a1 and a2), but the representativeness error is important for
this species (26 %), since one of its primary origins is emission of ships
passing nearby the coasts of Cap Corse. The local emissions due to
shipping activities at Ersa and anthropogenic activities at Es Pinar are
visible in the observations as frequent narrow (in time) peaks. These
activities are also visible in the simulations at Ersa. However, at Es Pinar
the model does not succeed in simulating them, as already observed to an even
larger extent for NOx. The slight overestimation of BC (5 %) in
the model for the Ersa site could be explained by the orographic
representativeness error. The same cloud effect seen for the sulfate
particles at Ersa is visible to a lesser extent for BC for the Ersa site as
well. Correlation between daily observations and the simulations was also
calculated. R2 is 0.51 and 0.56 for the Ersa station (instead of 0.36
and 0.42 for hourly values) and 0.81 and 0.65 for the Es Pinar station
(instead of 0.47 and 0.52 for hourly values) for sulfate and BC, respectively.
Similar to meteorological parameters, the model can reproduce the daily
concentration changes for these two species better than the hourly changes.

A description of each of the four schemes for the simulation of OAs
tested within the CHIMERE model for the Mediterranean area has been given in
Sect. 2. These four schemes are the CHIMERE standard scheme, the VBS scheme
with BSOA aging (noted as Standard VBS_ba), the VBS scheme without BSOA
aging (noted as Standard VBS_nba), and the modified VBS (noted as modified
VBS) scheme, which includes fragmentation and formation of nonvolatile
OA. For each scheme, four domains were used: one coarse domain
and three others nested inside the coarse one with increasing resolutions. As
before, the simulations from the finest domain are used for the comparisons.
The domain with the finest resolution (1 km) was used for the
representativeness tests. For each scheme, meteorological and boundary
conditions are the same; hence only the simulation of OAs and
subsequently the aggregation of anthropogenic PM emissions differ among
simulations.

5.1 Comparison of PM1 total organic aerosols concentration

Figure 7Compared time series of total PM1 organic aerosol (OA)
concentration at Ersa (top) and Es Pinar (bottom) – black: observations;
green: modified VBS; brown: standard VBS without BSOA (biogenic secondary
organic aerosol) aging; blue: CHIMERE standard; red: standard VBS with BSOA
aging. For each site the average of simulations is shown with circles in
corresponding colors and the observations with black squares. Shaded area for
Ersa presents the orographic representativeness error. Statistical data are
shown in Table 5.

Table 5Statistical data for hourly time series shown in Fig. 7. R is the
correlation coefficient between model and measurements, RMSE is the root mean
square error, mean_sim and mean_obs show the average, and SD_sim and
SD_obs show the standard deviation of simulations and observations,
respectively.

Figure 7 shows the time series of comparison of OAs for these
four schemes with the measurements at Ersa and Es Pinar. The circles beside
each plot show the average concentration for different time series; in
addition Table 5 shows the statistical parameters corresponding to these time
series. The observed OA concentration at the Ersa site measured by the ACSM
in the PM1 fraction has an average of 3.71 µg m−3
and that of the Es Pinar site measured by the AMS in the PM1
fraction a somewhat lower average of 2.88 µg m−3. The
difference between the two sites may be attributed to the fact that Cap Corse
is closer to both local and transported (continental) biogenic sources than
Es Pinar. The VBS scheme with BSOA aging greatly overestimates the OA with a bias of more than a factor of 3. As mentioned before, the
aging of biogenic aerosols in the VBS scheme usually results in an
overestimation in OAs
(Lane et al., 2008). With the BSOA
aging option turned off, the standard VBS scheme comes much closer to the
average of total OA concentration measured at both sites (relative biases of
below 50 %). The CHIMERE standard scheme also overestimates the mass
concentration of OAs, but to a lesser extent compared to the VBS
scheme with BSOA aging (bias of a bit less than a factor of 2). The
modified VBS scheme corresponds much better to observations (negative biases
about 20 %). The model results agree for both stations in this regard. At
Ersa, biases of +244, +91, +34, and −18 %, respectively, are observed
for the VBS standard scheme with BSOA aging, the CHIMERE scheme, the standard
VBS scheme without BSOA aging, and the VBS modified scheme. The corresponding
numbers are of +218, +98, +43, and −23 % for Es Pinar.

The daily correlation for the modified VBS scheme is 0.63 and 0.51 for Ersa
and Es Pinar, respectively (instead of 0.50 and 0.29 for hourly values). This
shows, again, that the model represents day-to-day changes better
than hourly variations. While the concentrations of both the modified VBS
scheme and the standard VBS scheme without BSOA aging correspond well with
the experimental data, other aspects such as origins of the formed OA and its oxidation state have to be considered before reaching any
conclusion about the robustness of these schemes.

The overestimation of secondary OA with the CHIMERE standard
scheme is a new feature, not apparent in previous studies
(Bessagnet et al., 2008; Hodzic and Jimenez, 2011; Petetin et al., 2014). A
previous comparison of CHIMERE with OA simulations using the
same scheme and coupled to MEGAN biogenic emissions resulted in a good
comparison or underestimation with OC observations over Europe from the
CARBOSOL project (Gelencsér et al., 2007) for summer
2003, when biogenic SOA was dominant. However, this comparison included only
one site close to the western Mediterranean basin (Montelibretti, Italy).
The overestimation of BSOA in the VBS scheme when the BSOA aging is
activated was documented for the USA on several occasions
(Robinson et al., 2007; Lane et al., 2008). Ultimately, it
cannot be excluded that the OA overestimation with both schemes at Ersa and
Es Pinar is also due to biogenic VOC (BVOC) overestimation. However, the available material
does not support this hypothesis: (i) OA overestimation is observed at two
independent, distant sites, (ii) local monoterpene underestimation is observed at both
these sites, and (iii) no evidence for MEGAN monoterpene overestimation is
available in literature and MEGAN isoprene overestimation over the western
Mediterranean area was ruled out by satellite observations
(Curci et al., 2010).

5.2 Total carbonaceous particle origins based on 14C measurements

The results of 14C measurements in carbonaceous aerosol filter
samples for the PM1 fraction at the Ersa and Es Pinar sites were
used in order to better discriminate between the modern and mostly biogenic
versus fossil and anthropogenic origin of OAs, and to compare it
to simulations with different OA schemes tested. It must be
noted that in the simulations, species are not separated automatically into
fossil and nonfossil parts; therefore these fractions need to be calculated as a
posttreatment of simulations, affecting each relevant particulate species to
both fractions. ASOA is considered to be in the fossil fraction and BSOA in
the nonfossil fraction. For carbonaceous aerosol, residential or domestic uses
are considered as nonfossil as they are mostly related to wood burning
(Sasser et al., 2012). Therefore, they are attributed to the nonfossil bin
(3.6 % for BC and 12.3 % for OC; Sasser et al., 2012). The nonfossil
contribution of ASOA and primary OA (POA) due to biofuel usage is ignored here, as it is
minor (< 5 %). No major biomass burning events were seen in the period
of this study, but minor contributions of this source cannot be excluded. It
should also be mentioned that the 14C measurements show the mass of
carbonaceous particles in each filter and therefore have a unit of µgC m−3, while the simulations show the total OAs in
µg m−3. In the comparisons that follow, it is pertinent to use
an organic matter (OM) ∕ OC conversion factor to be able to compare the 14C
measurements to simulations. For this purpose, an average OM ∕ OC factor
of 2 is used for the secondary aerosol fraction (both for the LVOOA and
SVOOA factors), while a factor of 1.3 is used for HOA according to
Aiken et al. (2008). However, the choice of the OM ∕ OC factor has a small effect on the
outcome of general comparisons since HOA values are marginal compared to
other factors and we are only interested in the relative contribution of
fossil and nonfossil factors.

Figure 8a for Ersa and 8b for Es Pinar show the average of all filters for
each scheme compared to the observations, while Fig. 9a and b show the
relative distribution of fossil and nonfossil sources at both sites. Among these
total averages, the distribution at Ersa is 81 % ± 1.5 % nonfossil
and 19 % ± 1.5 % fossil, and 67 % ± 3 % nonfossil versus
33 % ± 3 % fossil at Es Pinar. Apparently, biogenic contributions to OA
are dominant at both sites, but larger for the Ersa site.

While the comparison of averages for all schemes with Ersa measurements shows
that the modified VBS scheme is the closest in fossil and nonfossil partitioning
(19 %∕81 % ± 1 %), the CHIMERE standard scheme also
performs well when looking at the percentage of distribution for each
source (20 %∕80 % ± 1 %). The VBS scheme with BSOA aging shows
an underestimation in the percentage of fossil carbons
(16 %∕84 % ± 0.7 %), which can be due to the overestimation of
biogenic aging of SOAs in this scheme; conversely,
the VBS scheme without BSOA aging shows an important overestimation of the
fossil percentage (34 %∕66 % ± 2 %).

A distribution of 33 %∕67 % ± 3 % of the fossil and nonfossil
fractions is observed at Es Pinar. As for the Ersa site, the modified VBS
scheme is the closest to the observations (32 %∕68 % ± 2.5 %).
The CHIMERE standard scheme shows an underestimation of the fossil
contribution with a distribution of 28 %∕72 % ± 3 %. The
standard VBS scheme with BSOA aging underestimates the fossil contribution
with a distribution of 21 %∕79 % ± 1.5 %, while the standard
VBS scheme without BSOA aging largely overestimates the fossil contribution
(42 %∕58 % ± 2 %). These results show that the two sites differ
greatly when taking into account nearby sources and geographical conditions;
the Ersa site is an elevated rural station at the interface between the
marine boundary layer and the residual boundary layer, and the Es Pinar site
is a seaside station closer to anthropogenic sources. These differences
should normally be represented in the percentage of fossil carbon
concentrations in both observations and simulations. Although this
difference is noticeable in observations, and also in the modified VBS
scheme, it is less emphasized in the CHIMERE standard scheme and in the VBS
standard scheme with either parameterization of aging.

A more detailed look at individual filters for the modified VBS scheme for
the Ersa and Es Pinar sites is presented in Fig. 9a and b, respectively. The
tendencies from one day to another are in most cases not reproduced correctly
for both sites. Therefore while the average mass repartition of modern and
fossil sources is well represented by this scheme, the day-to-day variability
is not fully consistent with the measurements. This inconsistency is also
seen in the other tested schemes.

Figure 9Comparison of simulations to 14C measurements – (a) Ersa individual
filters; (b) Es Pinar individual filters. For each filter, measurements are
on the
left and modified VBS simulations are on the right. Dark or light brown shows fossil
and dark or light green shows nonfossil sources.

5.3 Volatility and oxidation state comparison with PMF results

The PMF results of the ACSM/AMS measurements give us the chance to learn more about the
oxidation state of the OAs (Michoud et al., 2017, for Ersa). The PMF analysis allows us to divide
PM1 OA measurements into different groups with
distinctive mass spectra corresponding to distinctive oxidation state
(Lanz et al., 2010). The most common
retrieved factors of such an analysis are HOA, SVOOA, and LVOOA. However, it does not give direct
information about the volatility distribution of each group. This makes the
PMF output difficult to compare with our model results, which give the
volatility distribution of OA but not its oxidative state.
Here, we first compare volatility distributions obtained with the four
aerosol schemes, and then try to attribute the simulated aerosol to the three
factors HOA, SVOOA, and LVOOA, in order to compare it to the observed
distributions. The three schemes based on the VBS scheme already distribute
aerosols in volatility bins (Robinson et al., 2007). The distribution to
LVOOA and SVOOA for these three schemes was performed mainly by taking into
account the saturation concentration, with the threshold chosen as a
saturation concentration C*≥ 1 µg m−3 for SVOOA
and C*≤ 0.1 µg m−3 for LVOOA
(Donahue et al., 2012). Primary OAs were considered to be in the HOA factor regardless of their
saturation concentration. For the CHIMERE standard scheme, each surrogate
species is associated with a saturation vapor pressure, which was used to
calculate the saturation concentration for each component at ambient
temperature.

The observations show that at Ersa, the LVOOA factor dominates the PMF
results (88 %), with 10 % SVOOA and a minor (only 2 %) contribution
of HOA. For the Es Pinar site the contribution of LVOOA drops to 75 %
and the contribution of SVOOA and HOA becomes somewhat larger (21 % and
4 %, respectively). As mentioned before, the Es Pinar site is more
influenced by local anthropogenic sources. Therefore, more local OA
emissions are expected, which corresponds to the increase in the HOA
percentage seen in the observations. These emissions are oxidized locally to
form SVOCs that fall in the SVOOA group, explaining the rise in the
percentage of this group.

Figure 10a and b show the relative distribution of all OAs in
seven volatility bins for all tested schemes for the Ersa and Es Pinar sites,
respectively, and for all tested schemes. For each scheme, the average for
the total period of the simulations was used to calculate the percentage in
each bin. The bins shown are in the range of 10−3–103µg m−3; all the aerosols with a volatility higher or lower than the
extremes are put in the last high or low bin, respectively.

These figures show that the percentage of aerosols in each volatility bin for
the two different sites is relatively similar. The CHIMERE standard scheme
distributes most of the aerosol in the three bins in
1–100 µg m−3 volatility ranges, which falls into the SVOOA
group obtained with PMF. This is due to the initial distribution of
volatilities of surrogate SVOC species used that best fit chamber
measurements, and the fact that there is no aging mechanism in this scheme to
make the aerosols less volatile. The 100 µg m−3 volatility bin
corresponds to SVOCs produced from isoprene and monoterpene oxidation and
presents the highest percentage for this scheme. The standard scheme also
produces 16 % of OAs in the LVOOA range (volatilities
corresponding to a C* between 0.001 and 0.1 µg m−3). The
standard VBS scheme with BSOA aging has only a small fraction of aerosol in the
LVOOA range (from SVOC emission aging) since, by construction of the scheme,
the most aged BSOA and ASOA aerosols fall in the 1 µg m−3
volatility bin, which is actually the bin with the highest percentage of OA.
The standard VBS scheme without BSOA aging presents relatively similar
results to the scheme with biogenic aging, with as expected larger
percentages for higher volatilities in the absence of aging. It also shows a
larger LVOOA fraction, probably because the lower total OA concentration
favors the contribution of lower-volatility SVOCs to the aerosol phase. On
the whole, the two schemes yield much too low LVOOA fractions as compared to
observations. The modified VBS scheme has a more realistic distribution into
the three oxidation groups. In this scheme, the highest percentage of OA
falls in the 10−3µg m−3 volatility bin, which is in the
LVOOA range. The rest of the aerosols are distributed almost equally in
volatility bins between 10−2 and 102µg m−3 with a
slight decrease in percentage in the higher-volatility bins. The percentage
in higher-volatility bins at Ersa are slightly lower than the ones at Es
Pinar, which could be explained by the stronger local sources at the Es Pinar
site with stronger primary SVOC emissions.

Figure 11a and b show the calculated HOA, LVOOA, and SVOOA groups in
simulations compared to the ones in observations at Ersa and at Es Pinar,
respectively. At Ersa, the results of the modified VBS scheme are consistent
with the observations with a slight underestimation of HOAs, which is more
visible at Es Pinar. The standard CHIMERE scheme leads to higher,
overestimated HOA values, as primary OA emissions are considered
nonvolatile. Only the modified VBS scheme succeeds in reproducing the major
contribution of LVOOAs at both sites. However, while staying close to
observations at Ersa, it seems to overestimate the formation of LVOOAs at Es
Pinar. The standard VBS schemes with or without BSOA aging greatly
underestimate the formation of LVOOAs, and as a counterpart overestimate the
formation of SVOOAs. Therefore, among the tested schemes, only the modified
VBS scheme allows us to represent the distribution of the different PMF factors
in a satisfying way.

In Sect. 5, we have highlighted the best performance of the modified VBS
scheme for OA simulation amongst the tested schemes by
comparison to observational data at two different sites in the western
Mediterranean basin. In the present section, we use this scheme in order to
simulate the OA distribution and its anthropogenic and biogenic
origins over the western Mediterranean basin during the SAFMED campaign.

Figure 12Average organic aerosol (OA) concentrations over the western
Mediterranean basin simulated from 10 July to 9 August 2013 with the modified
VBS scheme. The left column is near the surface, and the right column is for an altitude
between 300 and 450 m a.s.l. From top to bottom: (a–b) total organic aerosol
concentration (µg m−3); (c–d) BSOA (biogenic secondary OA) concentration
(µg m−3); (e–f) total ASOA (anthropogenic secondary OA) concentration
(µg m−3); (g–h) sum of POA (primary OA) and its subsequent oxidation products
(µg m−3). Results are from the D3 simulation (3 km horizontal resolution)
within the framed area and from the D10 simulation outside the framed area (10 km resolution).

Figure 12 shows a series of figures in which the left column always corresponds
to simulations near the surface, and the right column shows the same
concentration at an altitude of between 300 and 450 m (for marine grid
cells;
called for simplicity boundary layer, BL). Each row shows a different
component, with the first row corresponding to OA concentrations, second row to
biogenic OA concentrations, and third row to anthropogenic OA concentrations;
the
last row presents the sum of POA and all its subsequent oxidation products.
The larger part of each figure corresponds to D10 simulations, while the part
inside the black rectangle shows the D3 simulations, both showing the average
of the OAs for the whole simulation period (1 month from 10 July
to 9 August). Figure 13 shows the same type of figures for the
percentage of contribution of biogenic OAs, anthropogenic OAs, and the sum of
POAs (that is POA and POA oxidation products). The small differences at the
interface of the two domains is because the CHIMERE model is a one-way
chemistry–transport model: the simulations for the parent domain influence
the simulations for the nested domain; however, the inverse is not applied;
therefore any concentrations observed in the nested domain will not change
the concentrations seen in the parent domain.

Examining figures corresponding to OA concentrations (Fig. 12a and b), at the
surface level there is a region of high concentration of OA in the Gulf of
Genoa (between Genoa and Corsica) reaching nearly 4 µg m−3.
The high concentration in this area is less pronounced in the BL (Fig. 12b).
The concentration of biogenic OAs (Fig. 12c and d) is high over the basin as
well as over Europe, and less important over North Africa. The percentage of
contribution of this type of aerosol to the overall OA concentration is shown
in Fig. 13a and b. As expected from looking at the total concentration of
biogenic OAs, their contribution stays on average around 70 % over the
basin, but is lower in the Gulf of Genoa (about 60 % at surface, nearly
70 % at altitude). In this area the secondary anthropogenic OAs show a bit higher contributions compared to their contribution to
the rest of the domain (around 14 % instead of 12 %); also, the
contribution of POAs (and their subsequent oxidations) is
also quite high in this region (around 20 %). The areas between Corsica
and Marseilles and also the northern coast of Africa are main shipping
routes in the western Mediterranean basin, with high amounts of shipping
related emissions. They affect POA and its
semi-volatile oxidation products as shown in Fig. 12g and h, with
concentrations as high as 0.9 µg m−3 around Corsica and near
the African coast. This corresponds to a contribution of around 22 % over
Corsica and 25 % over the coast of Africa (Fig. 13e and f). These values
may actually be upper limits since shipping primary SVOC and IVOC emissions
were treated as those from other activity sectors (see Sect. 2.2.3), and
recent chamber study data suggest lower SOA yields from shipping emissions
(Pieber et al., 2016) than are used here. The influence of shipping emissions is
not visible in the BL because vertical mixing within the marine boundary
layer is weak, thus in the 300 to 400 m layer the contribution of biogenic
OA becomes dominant for the whole basin. Still, in the Gulf of Genoa, some
effects of anthropogenic influences are visible in the BL, which could be
linked to emissions originating from industrial sites and the harbor of Genoa
area, which are apparently better mixed vertically over the continental
convective boundary layer (in the coastal region with strong orography).
These anthropogenic emissions are visible in figures corresponding to
anthropogenic OA, formed from anthropogenic VOCs and especially aromatic
compounds (Fig. 12e and f for absolute concentrations and Fig. 13c and d for
relative percentages). While the average concentration of anthropogenic OAs
stay relatively low over the western Mediterranean basin (an average of
0.30 µg m−3 near the surface), they become more pronounced
around the Gulf of Genoa and the eastern part of the domain both near the
surface and in the BL. A contribution of about 12–15 % both near the
surface and in the BL is seen for this component above the Gulf of Genoa and
over the highly industrialized and densely populated Po Valley.

Three schemes for the simulation of OA were implemented and
tested along with the standard scheme in the CHIMERE chemistry–transport
model. The simulations from each of the four schemes were compared to
detailed experimental data obtained from two different stations in the
western Mediterranean area during the ChArMEx campaign in summer 2013. The
simulations were performed over 1 month in the summer of 2013 on four nested
domains with increasing resolutions, the largest one covering Europe and
northern Africa with a 30 km horizontal resolution, to the smallest one
focused on the Cap Corse area with a 1 km horizontal resolution.

For the comparisons of OA simulated with different schemes to observations,
we explored three different aspects: mass concentration, distribution with
respect to volatility and oxidative state of OA classes derived using PMF, and
14C measurements discriminating fossil or nonfossil origin.
Results show that the modified VBS scheme (i.e., including the fragmentation
and formation of nonvolatile OAs), better corresponds to the
observations at both sites, and this for all three aspects. The modified VBS
scheme succeeds at simulating the average concentration of OA for the
1-month campaign period with low bias (about −20 % at both sites), even
if the hourly variability is not perfectly displayed (as for other aerosol
components). Comparisons for OA precursors (isoprene and terpenes) and
isoprene oxidation products (sum of methyl vinyl ketone and methacroleine)
were performed and showed significant differences, which do not, however,
necessarily affect the model ability to form BSOA because the BVOC
measurements are representative for a local scale, while BSOA formation
occurs on a larger regional scale. Chrit et al. (2017) used a two-step
surrogate scheme for the simulation of the Ersa site measurements. They found
that their modified SOA simulation scheme corresponds well with the data,
with a correlation of 0.67 and a mean fractional bias of −0.15 for
daily values of the period between June and August 2013. For the period of
July to August 2013 for daily values, we find a correlation of 0.52 and a mean fractional bias of
−0.03 for the modified VBS scheme, which shows that both these schemes work
reasonably well for the simulated area.

Furthermore, the fossil–nonfossil distribution of OA was explored in our
different schemes. The modified VBS scheme corresponds better to available
data in this regard as well. It is also the only scheme among the four tested
that represents the distribution of the different PMF factors in a satisfying
way attributing the major OA fraction to LVOOA (but slightly overestimating
this part). The differences between the sites, especially the more local
anthropogenic character of the Es Pinar site (larger 14C fossil
fuel origin, larger SVOOA fraction, higher NOx concentration, which
is a good tracer for anthropogenic pollution) and the lower OA concentration
at this site are qualitatively simulated well with this scheme. While the
standard VBS scheme without BSOA aging is as close to mass and origin
comparisons as the modified VBS scheme, by construction, it does not include
the formation of LVOOA, resulting in an important overestimation of the
SVOOA at both sites.

A closer look at OA sources over the western Mediterranean basin simulated
with the modified VBS scheme, selected because of its good results (at least
for the summer of 2013 period and for this given region), shows that the OA
with biogenic origins is dominant in the whole basin. In areas between
Corsica and Marseilles, the Gulf of Genoa and also the northern coast of
Africa, the contribution of biogenic OAs is less than for other
parts. This fact points to the influence of shipping emissions for the areas
between Marseilles and Corsica and also the northern coast of Africa, which
can be seen in the contribution of POA and their oxidation products to the
formation of OA over the basin, even if this part may be overestimated in
the current simulations. For the Gulf of Genoa, a slightly higher contribution
of anthropogenic OAs was observed compared to other parts of
the domain. However, at a higher altitude, the contribution of the biogenic
sources becomes dominant in the whole basin, with a significant drop in the
contribution of POA over the basin and leaving only a small trace of
anthropogenic contribution in the Gulf of Genoa. This contribution is
attributed to ASOA rather than POA for the industrial area in northern
Italy, which is a persistent source of ASOA both near the surface and at
higher altitudes.

It would be useful to compare the precursor components for the formation of
OA such as isoprene and monoterpenes to multiple stations rather than only
two since the measurements for these species tend to have a more local
representativeness rather than a regional one; therefore multiple stations
spread in the domain would give the needed regional aspect of the
comparisons. Longer periods of simulations with comparisons to observations
are also necessary since processes leading to the formation of OA can
change in other seasons and especially in winter when the biogenic
contribution is much lower. Since airborne measurements for OA and biogenic
gas-phase precursors were also performed during the summer of 2014 in the
SAFMED+ campaign over different forested areas, it would be useful to
continue the simulations for this year to compare the results to in situ
measurements and airborne measurements at the same time.

This research has received funding
from the French National Research Agency (ANR) projects SAF-MED (grant ANR-15
12-BS06-0013). This work is part of the ChArMEx project supported by ADEME,
CEA, CNRS-INSU, and Météo France through the multidisciplinary
programme MISTRALS (Mediterranean Integrated Studies aT Regional And Local
Scales). The station at Ersa was partly supported by the CORSiCA project
funded by the Collectivité Territoriale de Corse through the Fonds
Européen de Développement Régional of the European Operational
Program 2007-2013 and the Contrat de Plan Etat-Région. The EEA is
acknowledged for air quality data for several stations in Europe which were
used for observation–simulation comparisons. The NCEP is acknowledged for the
meteorological input data used in the WRF meteorological model. E-OBS
data sets are acknowledged. The thesis work of Arineh Cholakian is supported by
ADEME, INERIS (with the support of the French ministry in charge of ecology),
and via the ANR SAF-MED project. Eric Hamonou and the ChArMEx team are
acknowledged for their great help in organizing the measurement campaign at
Ersa, as well as the MISTRALS management and accounting team. Jorge Pey is
currently granted with a Ramón y Cajal research contract (RYC-2013-14159)
from the Spanish Ministry of Economy, Industry and Competitiveness.

This work was performed using HPC resources from GENCI-CCRT (grant
2017-t2015017232).

Pankow, J. F.: Review and comparative analysis of the theories on
partitioning between the gas and aerosol particulate phases in the
atmosphere, Atmos. Environ., 21, 2275–2283,
https://doi.org/10.1016/0004-6981(87)90363-5, 1987.

In this work, four schemes for the simulation of organic aerosols in the western Mediterranean basin are added to the CHIMERE chemistry–transport model; the resulting simulations are then compared to measurements obtained from ChArMEx. It is concluded that the scheme taking into account the fragmentation and the formation of nonvolatile organic aerosols corresponds better to measurements; the major source of this aerosol in the western Mediterranean is found to be of biogenic origin.

In this work, four schemes for the simulation of organic aerosols in the western Mediterranean...